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Extracting and Visualizing Stock Data

Description

Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.

Table of Contents

  • Define a Function that Makes a Graph
  • Question 1: Use yfinance to Extract Stock Data
  • Question 2: Use Webscraping to Extract Tesla Revenue Data
  • Question 3: Use yfinance to Extract Stock Data
  • Question 4: Use Webscraping to Extract GME Revenue Data
  • Question 5: Plot Tesla Stock Graph
  • Question 6: Plot GameStop Stock Graph

Estimated Time Needed: 30 min


*Note*:- If you are working in IBM Cloud Watson Studio, please replace the command for installing nbformat from !pip install nbformat==4.2.0 to simply !pip install nbformat

In [1]:
!pip install yfinance==0.1.67
!mamba install bs4==4.10.0 -y
!pip install nbformat==4.2.0
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In [3]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots

In Python, you can ignore warnings using the warnings module. You can use the filterwarnings function to filter or ignore specific warning messages or categories.

In [4]:
import warnings
# Ignore all warnings
warnings.filterwarnings("ignore", category=FutureWarning)

Define Graphing Function¶

In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.

In [46]:
def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()

Question 1: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.

In [52]:
Tesla = yf.Ticker('TSLA')

Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to max so we get information for the maximum amount of time.

In [53]:
tesla_data = Tesla.history(period = 'max')
print(tesla_data)
                  Open        High         Low       Close     Volume  \
Date                                                                    
2010-06-29    1.266667    1.666667    1.169333    1.592667  281494500   
2010-06-30    1.719333    2.028000    1.553333    1.588667  257806500   
2010-07-01    1.666667    1.728000    1.351333    1.464000  123282000   
2010-07-02    1.533333    1.540000    1.247333    1.280000   77097000   
2010-07-06    1.333333    1.333333    1.055333    1.074000  103003500   
...                ...         ...         ...         ...        ...   
2024-01-22  212.259995  217.800003  206.270004  208.800003  117952500   
2024-01-23  211.300003  215.649994  207.750000  209.139999  106605900   
2024-01-24  211.880005  212.729996  206.770004  207.830002  123369900   
2024-01-25  189.699997  193.000000  180.059998  182.630005  198076800   
2024-01-26  185.500000  186.779999  182.100006  182.820099   93262176   

            Dividends  Stock Splits  
Date                                 
2010-06-29          0           0.0  
2010-06-30          0           0.0  
2010-07-01          0           0.0  
2010-07-02          0           0.0  
2010-07-06          0           0.0  
...               ...           ...  
2024-01-22          0           0.0  
2024-01-23          0           0.0  
2024-01-24          0           0.0  
2024-01-25          0           0.0  
2024-01-26          0           0.0  

[3418 rows x 7 columns]

Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.

In [10]:
tesla_data.reset_index(inplace=True)
tesla_data.head()
Out[10]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 1.266667 1.666667 1.169333 1.592667 281494500 0 0.0
1 2010-06-30 1.719333 2.028000 1.553333 1.588667 257806500 0 0.0
2 2010-07-01 1.666667 1.728000 1.351333 1.464000 123282000 0 0.0
3 2010-07-02 1.533333 1.540000 1.247333 1.280000 77097000 0 0.0
4 2010-07-06 1.333333 1.333333 1.055333 1.074000 103003500 0 0.0

Question 2: Use Webscraping to Extract Tesla Revenue Data¶

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm Save the text of the response as a variable named html_data.

In [11]:
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
html_data = requests.get(url).text

Parse the html data using beautiful_soup.

In [12]:
soup = BeautifulSoup(html_data,'html.parser')

Using BeautifulSoup or the read_html function extract the table with Tesla Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue.

Click here if you need help locating the table

Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab

soup.find_all("tbody")[1]

If you want to use the read_html function the table is located at index 1


In [43]:
tesla_revenue = pd.DataFrame(columns=['Date','Revenue'])

for row in soup.find_all("tbody")[1].find_all('tr'):
    col=row.find_all("td")
    date=col[0].text
    revenue=col[1].text
    
    tesla_revenue=tesla_revenue.append({"Date":date,"Revenue":revenue},ignore_index=True)
    tesla_revenue
#    
print(tesla_revenue)
          Date Revenue
0   2020-04-30  $1,021
1   2020-01-31  $2,194
2   2019-10-31  $1,439
3   2019-07-31  $1,286
4   2019-04-30  $1,548
..         ...     ...
57  2006-01-31  $1,667
58  2005-10-31    $534
59  2005-07-31    $416
60  2005-04-30    $475
61  2005-01-31    $709

[62 rows x 2 columns]

Execute the following line to remove the comma and dollar sign from the Revenue column.

In [19]:
tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"")

print(tesla_revenue)
          Date Revenue
0   2022-09-30   21454
1   2022-06-30   16934
2   2022-03-31   18756
3   2021-12-31   17719
4   2021-09-30   13757
5   2021-06-30   11958
6   2021-03-31   10389
7   2020-12-31   10744
8   2020-09-30    8771
9   2020-06-30    6036
10  2020-03-31    5985
11  2019-12-31    7384
12  2019-09-30    6303
13  2019-06-30    6350
14  2019-03-31    4541
15  2018-12-31    7226
16  2018-09-30    6824
17  2018-06-30    4002
18  2018-03-31    3409
19  2017-12-31    3288
20  2017-09-30    2985
21  2017-06-30    2790
22  2017-03-31    2696
23  2016-12-31    2285
24  2016-09-30    2298
25  2016-06-30    1270
26  2016-03-31    1147
27  2015-12-31    1214
28  2015-09-30     937
29  2015-06-30     955
30  2015-03-31     940
31  2014-12-31     957
32  2014-09-30     852
33  2014-06-30     769
34  2014-03-31     621
35  2013-12-31     615
36  2013-09-30     431
37  2013-06-30     405
38  2013-03-31     562
39  2012-12-31     306
40  2012-09-30      50
41  2012-06-30      27
42  2012-03-31      30
43  2011-12-31      39
44  2011-09-30      58
45  2011-06-30      58
46  2011-03-31      49
47  2010-12-31      36
48  2010-09-30      31
49  2010-06-30      28
50  2010-03-31      21
52  2009-09-30      46
53  2009-06-30      27

Execute the following lines to remove an null or empty strings in the Revenue column.

In [20]:
tesla_revenue.dropna(inplace=True)

tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]

print(tesla_revenue)
          Date Revenue
0   2022-09-30   21454
1   2022-06-30   16934
2   2022-03-31   18756
3   2021-12-31   17719
4   2021-09-30   13757
5   2021-06-30   11958
6   2021-03-31   10389
7   2020-12-31   10744
8   2020-09-30    8771
9   2020-06-30    6036
10  2020-03-31    5985
11  2019-12-31    7384
12  2019-09-30    6303
13  2019-06-30    6350
14  2019-03-31    4541
15  2018-12-31    7226
16  2018-09-30    6824
17  2018-06-30    4002
18  2018-03-31    3409
19  2017-12-31    3288
20  2017-09-30    2985
21  2017-06-30    2790
22  2017-03-31    2696
23  2016-12-31    2285
24  2016-09-30    2298
25  2016-06-30    1270
26  2016-03-31    1147
27  2015-12-31    1214
28  2015-09-30     937
29  2015-06-30     955
30  2015-03-31     940
31  2014-12-31     957
32  2014-09-30     852
33  2014-06-30     769
34  2014-03-31     621
35  2013-12-31     615
36  2013-09-30     431
37  2013-06-30     405
38  2013-03-31     562
39  2012-12-31     306
40  2012-09-30      50
41  2012-06-30      27
42  2012-03-31      30
43  2011-12-31      39
44  2011-09-30      58
45  2011-06-30      58
46  2011-03-31      49
47  2010-12-31      36
48  2010-09-30      31
49  2010-06-30      28
50  2010-03-31      21
52  2009-09-30      46
53  2009-06-30      27

Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.

In [21]:
tesla_revenue.tail(5)
Out[21]:
Date Revenue
48 2010-09-30 31
49 2010-06-30 28
50 2010-03-31 21
52 2009-09-30 46
53 2009-06-30 27

Question 3: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.

In [22]:
GameStop = yf.Ticker('GME')

Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to max so we get information for the maximum amount of time.

In [26]:
gme_data = GameStop.history(period = 'max')
print(gme_data)
                 Open       High        Low      Close    Volume  Dividends  \
Date                                                                          
2002-02-13   1.620129   1.693350   1.603296   1.691667  76216000        0.0   
2002-02-14   1.712707   1.716074   1.670626   1.683250  11021600        0.0   
2002-02-15   1.683250   1.687458   1.658001   1.674834   8389600        0.0   
2002-02-19   1.666418   1.666418   1.578047   1.607504   7410400        0.0   
2002-02-20   1.615921   1.662210   1.603296   1.662210   6892800        0.0   
...               ...        ...        ...        ...       ...        ...   
2024-01-22  14.500000  15.160000  14.300000  14.900000   3606500        0.0   
2024-01-23  15.000000  15.020000  14.050000  14.180000   3495300        0.0   
2024-01-24  14.280000  14.380000  13.820000  13.950000   2513800        0.0   
2024-01-25  13.970000  14.540000  13.920000  14.520000   3631600        0.0   
2024-01-26  14.440000  14.720000  14.420000  14.520100   1398538        0.0   

            Stock Splits  
Date                      
2002-02-13           0.0  
2002-02-14           0.0  
2002-02-15           0.0  
2002-02-19           0.0  
2002-02-20           0.0  
...                  ...  
2024-01-22           0.0  
2024-01-23           0.0  
2024-01-24           0.0  
2024-01-25           0.0  
2024-01-26           0.0  

[5526 rows x 7 columns]

Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.

In [27]:
gme_data.reset_index(inplace=True)
gme_data.head(5)
Out[27]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 1.620129 1.693350 1.603296 1.691667 76216000 0.0 0.0
1 2002-02-14 1.712707 1.716074 1.670626 1.683250 11021600 0.0 0.0
2 2002-02-15 1.683250 1.687458 1.658001 1.674834 8389600 0.0 0.0
3 2002-02-19 1.666418 1.666418 1.578047 1.607504 7410400 0.0 0.0
4 2002-02-20 1.615921 1.662210 1.603296 1.662210 6892800 0.0 0.0

Question 4: Use Webscraping to Extract GME Revenue Data¶

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named html_data.

In [30]:
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"
html_data = requests.get(url).text

Parse the html data using beautiful_soup.

In [31]:
soup = BeautifulSoup(html_data,"html.parser")

Using BeautifulSoup or the read_html function extract the table with GameStop Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column using a method similar to what you did in Question 2.

Click here if you need help locating the table

Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab

soup.find_all("tbody")[1]

If you want to use the read_html function the table is located at index 1


In [34]:
gme_revenue = pd.DataFrame(columns=['Date','Revenue'])

for row in soup.find_all("tbody")[1].find_all('tr'):
    col = row.find_all('td')
    date = col[0].text
    revenue = col[1].text
    
    gme_revenue = gme_revenue.append({'Date':date,'Revenue':revenue},ignore_index = True)
    gme_revenue
    
#Remove comma and dollar sign
gme_revenue["Revenue"] = gme_revenue['Revenue'].str.replace(',|\$',"")

print(gme_revenue)
          Date Revenue
0   2020-04-30    1021
1   2020-01-31    2194
2   2019-10-31    1439
3   2019-07-31    1286
4   2019-04-30    1548
..         ...     ...
57  2006-01-31    1667
58  2005-10-31     534
59  2005-07-31     416
60  2005-04-30     475
61  2005-01-31     709

[62 rows x 2 columns]

Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.

In [35]:
gme_revenue.tail(5)
Out[35]:
Date Revenue
57 2006-01-31 1667
58 2005-10-31 534
59 2005-07-31 416
60 2005-04-30 475
61 2005-01-31 709

Question 5: Plot Tesla Stock Graph¶

Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(tesla_data, tesla_revenue, 'Tesla'). Note the graph will only show data upto June 2021.

In [54]:
make_graph(tesla_data, tesla_revenue,'Tesla')
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/tmp/ipykernel_2370/3781297139.py in <module>
----> 1 make_graph(tesla_data, tesla_revenue,'Tesla')

/tmp/ipykernel_2370/2068038883.py in make_graph(stock_data, revenue_data, stock)
      1 def make_graph(stock_data, revenue_data, stock):
      2     fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
----> 3     stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
      4     revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
      5     fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)

~/conda/envs/python/lib/python3.7/site-packages/pandas/core/generic.py in __getattr__(self, name)
   5485         ):
   5486             return self[name]
-> 5487         return object.__getattribute__(self, name)
   5488 
   5489     def __setattr__(self, name: str, value) -> None:

AttributeError: 'DataFrame' object has no attribute 'Date'

Question 6: Plot GameStop Stock Graph¶

Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop'). Note the graph will only show data upto June 2021.

In [40]:
make_graph(gme_data, gme_revenue, 'GameStop')

About the Authors:

Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

Azim Hirjani

Change Log¶

Date (YYYY-MM-DD) Version Changed By Change Description
2022-02-28 1.2 Lakshmi Holla Changed the URL of GameStop
2020-11-10 1.1 Malika Singla Deleted the Optional part
2020-08-27 1.0 Malika Singla Added lab to GitLab

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